Overview

Dataset statistics

Number of variables14
Number of observations2194
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory257.1 KiB
Average record size in memory120.0 B

Variable types

Categorical2
Numeric12

Alerts

Hb is highly overall correlated with SexHigh correlation
RBC is highly overall correlated with MCHCHigh correlation
MCHC is highly overall correlated with RBCHigh correlation
hbA is highly overall correlated with Thalassemia TypeHigh correlation
hbA2 is highly overall correlated with Thalassemia TypeHigh correlation
Sex is highly overall correlated with HbHigh correlation
Thalassemia Type is highly overall correlated with hbA and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-10-22 09:51:25.514731
Analysis finished2023-10-22 09:51:40.292522
Duration14.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size141.4 KiB
1
1267 
0
927 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2194
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Length

2023-10-22T15:51:40.358699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T15:51:40.475208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Most occurring characters

ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2194
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1267
57.7%
0 927
42.3%

Age
Real number (ℝ)

Distinct1592
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.460077
Minimum4.55
Maximum49.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:40.616279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.55
5-th percentile5.6965
Q110.1
median19.135
Q327.5475
95-th percentile42.7935
Maximum49.91
Range45.36
Interquartile range (IQR)17.4475

Descriptive statistics

Standard deviation11.459547
Coefficient of variation (CV)0.56009303
Kurtosis-0.57240953
Mean20.460077
Median Absolute Deviation (MAD)8.765
Skewness0.56720327
Sum44889.41
Variance131.32121
MonotonicityNot monotonic
2023-10-22T15:51:40.752845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.16 6
 
0.3%
20.54 5
 
0.2%
8.03 5
 
0.2%
22.99 5
 
0.2%
5.65 4
 
0.2%
10.07 4
 
0.2%
10.37 4
 
0.2%
6.89 4
 
0.2%
5.82 4
 
0.2%
7.43 4
 
0.2%
Other values (1582) 2149
97.9%
ValueCountFrequency (%)
4.55 3
0.1%
4.56 1
 
< 0.1%
4.59 1
 
< 0.1%
4.6 2
0.1%
4.61 1
 
< 0.1%
4.64 1
 
< 0.1%
4.65 2
0.1%
4.68 1
 
< 0.1%
4.69 3
0.1%
4.71 2
0.1%
ValueCountFrequency (%)
49.91 1
< 0.1%
49.89 1
< 0.1%
49.87 1
< 0.1%
49.84 1
< 0.1%
49.7 1
< 0.1%
49.52 1
< 0.1%
49.49 1
< 0.1%
49.47 1
< 0.1%
48.84 1
< 0.1%
48.77 1
< 0.1%

Hb
Real number (ℝ)

HIGH CORRELATION 

Distinct298
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5864768
Minimum7.01
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:40.888066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.01
5-th percentile7.26
Q18.07
median8.57
Q39.14
95-th percentile9.87
Maximum10
Range2.99
Interquartile range (IQR)1.07

Descriptive statistics

Standard deviation0.7682916
Coefficient of variation (CV)0.089476933
Kurtosis-0.77548186
Mean8.5864768
Median Absolute Deviation (MAD)0.53
Skewness-0.032941784
Sum18838.73
Variance0.59027199
MonotonicityNot monotonic
2023-10-22T15:51:41.032188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 20
 
0.9%
8.24 19
 
0.9%
8.17 19
 
0.9%
8.09 18
 
0.8%
8.5 16
 
0.7%
8.28 16
 
0.7%
8.89 16
 
0.7%
8.27 15
 
0.7%
8.87 15
 
0.7%
8.11 15
 
0.7%
Other values (288) 2025
92.3%
ValueCountFrequency (%)
7.01 4
0.2%
7.02 3
 
0.1%
7.03 6
0.3%
7.04 3
 
0.1%
7.05 3
 
0.1%
7.06 5
0.2%
7.07 8
0.4%
7.08 5
0.2%
7.09 6
0.3%
7.1 2
 
0.1%
ValueCountFrequency (%)
10 5
 
0.2%
9.99 14
0.6%
9.98 10
0.5%
9.97 7
0.3%
9.96 10
0.5%
9.95 10
0.5%
9.94 7
0.3%
9.93 11
0.5%
9.92 1
 
< 0.1%
9.91 11
0.5%

PCV
Real number (ℝ)

Distinct1068
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.862976
Minimum20.18
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:41.174744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.18
5-th percentile23.3765
Q127.03
median30.07
Q333.0175
95-th percentile35.3435
Maximum36
Range15.82
Interquartile range (IQR)5.9875

Descriptive statistics

Standard deviation3.7245794
Coefficient of variation (CV)0.12472231
Kurtosis-0.81971044
Mean29.862976
Median Absolute Deviation (MAD)2.99
Skewness-0.27543843
Sum65519.37
Variance13.872492
MonotonicityNot monotonic
2023-10-22T15:51:41.321712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.22 7
 
0.3%
25.8 7
 
0.3%
32.38 7
 
0.3%
33.24 7
 
0.3%
32.71 6
 
0.3%
30.69 6
 
0.3%
27.8 6
 
0.3%
32.16 6
 
0.3%
29.36 6
 
0.3%
25.67 6
 
0.3%
Other values (1058) 2130
97.1%
ValueCountFrequency (%)
20.18 1
< 0.1%
20.21 1
< 0.1%
20.32 1
< 0.1%
20.34 1
< 0.1%
20.4 1
< 0.1%
20.48 1
< 0.1%
20.68 1
< 0.1%
20.69 1
< 0.1%
20.75 1
< 0.1%
20.85 1
< 0.1%
ValueCountFrequency (%)
36 1
 
< 0.1%
35.99 1
 
< 0.1%
35.98 1
 
< 0.1%
35.96 1
 
< 0.1%
35.95 4
0.2%
35.94 1
 
< 0.1%
35.9 2
0.1%
35.89 3
0.1%
35.88 1
 
< 0.1%
35.87 1
 
< 0.1%

RBC
Real number (ℝ)

HIGH CORRELATION 

Distinct301
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9843118
Minimum1.5
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:41.461438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile1.6465
Q12.22
median3
Q33.72
95-th percentile4.34
Maximum4.5
Range3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.86756017
Coefficient of variation (CV)0.29070695
Kurtosis-1.2192116
Mean2.9843118
Median Absolute Deviation (MAD)0.75
Skewness-0.0025156839
Sum6547.58
Variance0.75266065
MonotonicityNot monotonic
2023-10-22T15:51:41.597012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.98 15
 
0.7%
2.59 15
 
0.7%
1.6 15
 
0.7%
3.42 14
 
0.6%
1.91 13
 
0.6%
3 13
 
0.6%
3.2 13
 
0.6%
3.84 13
 
0.6%
3.34 12
 
0.5%
3.5 12
 
0.5%
Other values (291) 2059
93.8%
ValueCountFrequency (%)
1.5 2
 
0.1%
1.51 6
0.3%
1.52 6
0.3%
1.53 9
0.4%
1.54 7
0.3%
1.55 7
0.3%
1.56 10
0.5%
1.57 10
0.5%
1.58 7
0.3%
1.59 7
0.3%
ValueCountFrequency (%)
4.5 5
0.2%
4.49 6
0.3%
4.48 4
0.2%
4.47 9
0.4%
4.46 4
0.2%
4.45 4
0.2%
4.44 8
0.4%
4.43 6
0.3%
4.42 4
0.2%
4.41 6
0.3%

MCV
Real number (ℝ)

Distinct1792
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.960561
Minimum22.58
Maximum84.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:41.730140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.58
5-th percentile33.5825
Q149.945
median62.425
Q373.7875
95-th percentile82.917
Maximum84.96
Range62.38
Interquartile range (IQR)23.8425

Descriptive statistics

Standard deviation15.412753
Coefficient of variation (CV)0.25283155
Kurtosis-0.83785974
Mean60.960561
Median Absolute Deviation (MAD)11.82
Skewness-0.35323621
Sum133747.47
Variance237.55296
MonotonicityNot monotonic
2023-10-22T15:51:41.866712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.18 5
 
0.2%
59.39 4
 
0.2%
72.65 4
 
0.2%
52.79 3
 
0.1%
71.36 3
 
0.1%
60.48 3
 
0.1%
63.39 3
 
0.1%
77.49 3
 
0.1%
71.61 3
 
0.1%
82.48 3
 
0.1%
Other values (1782) 2160
98.5%
ValueCountFrequency (%)
22.58 1
< 0.1%
22.79 1
< 0.1%
23.99 1
< 0.1%
24.32 1
< 0.1%
24.61 1
< 0.1%
24.85 1
< 0.1%
25.14 1
< 0.1%
25.25 1
< 0.1%
25.44 1
< 0.1%
25.51 1
< 0.1%
ValueCountFrequency (%)
84.96 1
< 0.1%
84.91 1
< 0.1%
84.89 1
< 0.1%
84.88 1
< 0.1%
84.85 1
< 0.1%
84.84 2
0.1%
84.83 1
< 0.1%
84.81 1
< 0.1%
84.8 1
< 0.1%
84.77 1
< 0.1%

MCH
Real number (ℝ)

Distinct954
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.351226
Minimum16.01
Maximum26.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:42.006305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.01
5-th percentile16.5665
Q118.56
median21.33
Q324.0675
95-th percentile26.417
Maximum26.99
Range10.98
Interquartile range (IQR)5.5075

Descriptive statistics

Standard deviation3.1698549
Coefficient of variation (CV)0.14846243
Kurtosis-1.2200827
Mean21.351226
Median Absolute Deviation (MAD)2.76
Skewness0.06752578
Sum46844.59
Variance10.04798
MonotonicityNot monotonic
2023-10-22T15:51:42.143390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.58 8
 
0.4%
24.01 7
 
0.3%
17.66 7
 
0.3%
18.94 7
 
0.3%
23.41 6
 
0.3%
20.46 6
 
0.3%
19.72 6
 
0.3%
19.7 6
 
0.3%
20.59 6
 
0.3%
17.42 6
 
0.3%
Other values (944) 2129
97.0%
ValueCountFrequency (%)
16.01 1
 
< 0.1%
16.02 2
 
0.1%
16.03 2
 
0.1%
16.04 3
0.1%
16.05 2
 
0.1%
16.06 1
 
< 0.1%
16.07 6
0.3%
16.08 3
0.1%
16.09 1
 
< 0.1%
16.1 1
 
< 0.1%
ValueCountFrequency (%)
26.99 1
 
< 0.1%
26.98 1
 
< 0.1%
26.97 2
0.1%
26.96 3
0.1%
26.95 2
0.1%
26.94 4
0.2%
26.93 1
 
< 0.1%
26.92 3
0.1%
26.91 4
0.2%
26.89 3
0.1%

MCHC
Real number (ℝ)

HIGH CORRELATION 

Distinct1444
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.583017
Minimum16.03
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:42.279974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.03
5-th percentile18.3465
Q121.95
median27.66
Q333.405
95-th percentile43.234
Maximum60
Range43.97
Interquartile range (IQR)11.455

Descriptive statistics

Standard deviation7.8996649
Coefficient of variation (CV)0.27637617
Kurtosis0.49719964
Mean28.583017
Median Absolute Deviation (MAD)5.72
Skewness0.79139897
Sum62711.14
Variance62.404706
MonotonicityNot monotonic
2023-10-22T15:51:42.545878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.24 7
 
0.3%
32.06 5
 
0.2%
32.03 5
 
0.2%
33.95 5
 
0.2%
20 5
 
0.2%
25.95 5
 
0.2%
18.8 5
 
0.2%
32.17 5
 
0.2%
19.05 5
 
0.2%
18.01 4
 
0.2%
Other values (1434) 2143
97.7%
ValueCountFrequency (%)
16.03 1
< 0.1%
16.78 1
< 0.1%
16.86 1
< 0.1%
16.88 1
< 0.1%
16.94 1
< 0.1%
17 1
< 0.1%
17.02 2
0.1%
17.22 1
< 0.1%
17.29 1
< 0.1%
17.32 1
< 0.1%
ValueCountFrequency (%)
60 1
< 0.1%
58.54 1
< 0.1%
57.77 1
< 0.1%
57.2 1
< 0.1%
56.89 1
< 0.1%
56.85 1
< 0.1%
56.49 1
< 0.1%
56.35 1
< 0.1%
56.17 1
< 0.1%
55.77 1
< 0.1%

RDW
Real number (ℝ)

Distinct669
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.512015
Minimum30
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:42.673457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30.34
Q131.7525
median33.565
Q335.26
95-th percentile36.6535
Maximum37
Range7
Interquartile range (IQR)3.5075

Descriptive statistics

Standard deviation2.0142118
Coefficient of variation (CV)0.06010417
Kurtosis-1.1670378
Mean33.512015
Median Absolute Deviation (MAD)1.725
Skewness-0.030479595
Sum73525.36
Variance4.0570492
MonotonicityNot monotonic
2023-10-22T15:51:42.812581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.43 11
 
0.5%
36.57 10
 
0.5%
35.27 10
 
0.5%
33.8 9
 
0.4%
30.42 9
 
0.4%
33.48 8
 
0.4%
33.76 8
 
0.4%
33.47 8
 
0.4%
31.17 8
 
0.4%
30.31 8
 
0.4%
Other values (659) 2105
95.9%
ValueCountFrequency (%)
30 3
0.1%
30.01 3
0.1%
30.02 5
0.2%
30.03 5
0.2%
30.04 2
 
0.1%
30.05 2
 
0.1%
30.06 3
0.1%
30.07 3
0.1%
30.08 2
 
0.1%
30.09 1
 
< 0.1%
ValueCountFrequency (%)
37 1
 
< 0.1%
36.99 5
0.2%
36.98 3
0.1%
36.97 5
0.2%
36.96 5
0.2%
36.95 2
 
0.1%
36.94 4
0.2%
36.93 5
0.2%
36.92 2
 
0.1%
36.91 3
0.1%

WBC
Real number (ℝ)

Distinct682
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3953965
Minimum4.9
Maximum11.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:42.953133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile5.26
Q16.65
median8.335
Q310.1575
95-th percentile11.6535
Maximum11.99
Range7.09
Interquartile range (IQR)3.5075

Descriptive statistics

Standard deviation2.0408888
Coefficient of variation (CV)0.24309618
Kurtosis-1.181449
Mean8.3953965
Median Absolute Deviation (MAD)1.745
Skewness0.048456673
Sum18419.5
Variance4.1652272
MonotonicityNot monotonic
2023-10-22T15:51:43.095825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.02 11
 
0.5%
7.9 10
 
0.5%
7.27 9
 
0.4%
5.82 9
 
0.4%
10.36 9
 
0.4%
6.82 8
 
0.4%
10.71 8
 
0.4%
10.31 8
 
0.4%
6.65 8
 
0.4%
11.16 8
 
0.4%
Other values (672) 2106
96.0%
ValueCountFrequency (%)
4.9 2
 
0.1%
4.91 5
0.2%
4.92 3
0.1%
4.93 3
0.1%
4.94 7
0.3%
4.95 4
0.2%
4.96 2
 
0.1%
4.97 2
 
0.1%
4.98 1
 
< 0.1%
4.99 4
0.2%
ValueCountFrequency (%)
11.99 6
0.3%
11.98 3
0.1%
11.97 6
0.3%
11.95 4
0.2%
11.94 4
0.2%
11.93 6
0.3%
11.92 3
0.1%
11.91 2
 
0.1%
11.9 2
 
0.1%
11.89 1
 
< 0.1%

Plt
Real number (ℝ)

Distinct2135
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.19869
Minimum98.23
Maximum508.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:43.235717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.23
5-th percentile119.788
Q1202.545
median307.275
Q3404.845
95-th percentile488.3075
Maximum508.76
Range410.53
Interquartile range (IQR)202.3

Descriptive statistics

Standard deviation117.7599
Coefficient of variation (CV)0.38584667
Kurtosis-1.1862546
Mean305.19869
Median Absolute Deviation (MAD)101.045
Skewness-0.015455972
Sum669605.92
Variance13867.394
MonotonicityNot monotonic
2023-10-22T15:51:43.371534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 2
 
0.1%
140.64 2
 
0.1%
268.13 2
 
0.1%
307.84 2
 
0.1%
416.26 2
 
0.1%
227.12 2
 
0.1%
458.49 2
 
0.1%
110.27 2
 
0.1%
418.92 2
 
0.1%
229.78 2
 
0.1%
Other values (2125) 2174
99.1%
ValueCountFrequency (%)
98.23 1
< 0.1%
98.31 1
< 0.1%
98.37 1
< 0.1%
98.4 1
< 0.1%
98.71 1
< 0.1%
98.93 1
< 0.1%
99.02 1
< 0.1%
99.05 1
< 0.1%
99.08 1
< 0.1%
99.53 1
< 0.1%
ValueCountFrequency (%)
508.76 1
< 0.1%
508.71 1
< 0.1%
508.43 1
< 0.1%
507.6 1
< 0.1%
507.51 1
< 0.1%
507.47 1
< 0.1%
507.39 1
< 0.1%
507.34 1
< 0.1%
507.25 1
< 0.1%
507.21 1
< 0.1%

hbA
Real number (ℝ)

HIGH CORRELATION 

Distinct1439
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.799599
Minimum57.4
Maximum97.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:43.505767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57.4
5-th percentile59.2865
Q163.91
median69.49
Q376.74
95-th percentile97.337
Maximum97.98
Range40.58
Interquartile range (IQR)12.83

Descriptive statistics

Standard deviation13.004407
Coefficient of variation (CV)0.17621244
Kurtosis-0.86376186
Mean73.799599
Median Absolute Deviation (MAD)6.17
Skewness0.79778264
Sum161916.32
Variance169.11461
MonotonicityNot monotonic
2023-10-22T15:51:43.651344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.69 8
 
0.4%
97.85 6
 
0.3%
74.95 5
 
0.2%
94.02 5
 
0.2%
65.04 5
 
0.2%
68.36 5
 
0.2%
60.94 5
 
0.2%
61.32 5
 
0.2%
97.37 5
 
0.2%
59.53 4
 
0.2%
Other values (1429) 2141
97.6%
ValueCountFrequency (%)
57.4 1
< 0.1%
57.61 1
< 0.1%
57.66 1
< 0.1%
57.68 2
0.1%
57.7 2
0.1%
57.76 1
< 0.1%
57.83 1
< 0.1%
57.87 1
< 0.1%
57.89 1
< 0.1%
57.9 1
< 0.1%
ValueCountFrequency (%)
97.98 2
0.1%
97.97 2
0.1%
97.94 1
 
< 0.1%
97.93 1
 
< 0.1%
97.92 1
 
< 0.1%
97.9 2
0.1%
97.89 1
 
< 0.1%
97.88 3
0.1%
97.87 2
0.1%
97.86 2
0.1%

hbA2
Real number (ℝ)

HIGH CORRELATION 

Distinct396
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2287967
Minimum2
Maximum7.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.3 KiB
2023-10-22T15:51:43.793920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.47
median2.93
Q33.45
95-th percentile6.1335
Maximum7.19
Range5.19
Interquartile range (IQR)0.98

Descriptive statistics

Standard deviation1.1488562
Coefficient of variation (CV)0.35581559
Kurtosis2.7044327
Mean3.2287967
Median Absolute Deviation (MAD)0.49
Skewness1.7589164
Sum7083.98
Variance1.3198706
MonotonicityNot monotonic
2023-10-22T15:51:43.938529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.66 20
 
0.9%
3.23 19
 
0.9%
2.63 19
 
0.9%
2.21 18
 
0.8%
3.42 18
 
0.8%
2.18 18
 
0.8%
2.16 17
 
0.8%
2.29 17
 
0.8%
2.24 17
 
0.8%
3.47 17
 
0.8%
Other values (386) 2014
91.8%
ValueCountFrequency (%)
2 5
 
0.2%
2.01 10
0.5%
2.02 13
0.6%
2.03 15
0.7%
2.04 13
0.6%
2.05 6
 
0.3%
2.06 16
0.7%
2.07 6
 
0.3%
2.08 8
0.4%
2.09 9
0.4%
ValueCountFrequency (%)
7.19 1
 
< 0.1%
7.18 5
0.2%
7.16 4
0.2%
7.15 4
0.2%
7.14 2
 
0.1%
7.13 5
0.2%
7.09 1
 
< 0.1%
7.07 2
 
0.1%
7.06 1
 
< 0.1%
7.05 1
 
< 0.1%

Thalassemia Type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size141.4 KiB
2
1027 
3
622 
1
339 
0
206 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2194
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row3
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Length

2023-10-22T15:51:44.062440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T15:51:44.163686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Most occurring characters

ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2194
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2194
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1027
46.8%
3 622
28.4%
1 339
 
15.5%
0 206
 
9.4%

Interactions

2023-10-22T15:51:38.900887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:25.818078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.905304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.030266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.301534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.408896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.608938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.759859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.043849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.255068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.440896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.559826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.986438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:25.900640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.991863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.119766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.387558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.495888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.705475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.855379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.137396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.350605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.527447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.655346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.076011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:25.991643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.082426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.347205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.481106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.587435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.804071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.960505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.234222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.450121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.620012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.754884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.178531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.089206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.182979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.445776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.578673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.686964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.904211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.074635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.336775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.554668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.721548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.861630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.270054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.179714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.275521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.542701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.670239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.783506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.998224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.178836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.437304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.653234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.815696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.962159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.364594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.269283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.370851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.639264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.762129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.883054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.092306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.272041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.536645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.752742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.910253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.061759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.450143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.356395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.468381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.735783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.854888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.982594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.178512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.361314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.628205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.848479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.999252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.158282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.537564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.439938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.556965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.823673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.941413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.082700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.269553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.583489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.720727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.943017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.086779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.251582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.632113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.540063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.653444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:28.922246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.036973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.185240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.371063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.679018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.824255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.047093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.184308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.355119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.728648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.635614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.751008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.020812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.134549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.277770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.476229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.773581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:34.940750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.147971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.280854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.590241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.812429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.718203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.840118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.107381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.221090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.374326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.566758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.857118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.040309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.239777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.366734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.685802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:39.909958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:26.817713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:27.938694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:29.209949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:30.321306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:31.490881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:32.666304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:33.955684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:35.155524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:36.345317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:37.468303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-22T15:51:38.802342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-22T15:51:44.254247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2SexThalassemia Type
Age1.000-0.0580.004-0.0070.0090.016-0.010-0.001-0.0070.0120.0300.0270.0490.000
Hb-0.0581.0000.263-0.0050.019-0.0060.2720.0060.032-0.0060.0090.0340.6820.021
PCV0.0040.2631.0000.0030.025-0.0020.0680.0180.0360.027-0.0170.0150.2520.000
RBC-0.007-0.0050.0031.000-0.3320.062-0.9420.0020.015-0.022-0.056-0.0150.0430.042
MCV0.0090.0190.025-0.3321.000-0.0140.3160.007-0.018-0.0260.0040.0090.0280.000
MCH0.016-0.006-0.0020.062-0.0141.000-0.0540.004-0.0270.0020.0180.0160.0310.040
MCHC-0.0100.2720.068-0.9420.316-0.0541.0000.0050.0010.0220.0460.0230.1930.022
RDW-0.0010.0060.0180.0020.0070.0040.0051.0000.008-0.0130.0020.0180.0000.000
WBC-0.0070.0320.0360.015-0.018-0.0270.0010.0081.000-0.0100.0090.0230.0000.000
Plt0.012-0.0060.027-0.022-0.0260.0020.022-0.013-0.0101.0000.0130.0080.0000.014
hbA0.0300.009-0.017-0.0560.0040.0180.0460.0020.0090.0131.0000.2100.0000.674
hbA20.0270.0340.015-0.0150.0090.0160.0230.0180.0230.0080.2101.0000.0000.571
Sex0.0490.6820.2520.0430.0280.0310.1930.0000.0000.0000.0000.0001.0000.035
Thalassemia Type0.0000.0210.0000.0420.0000.0400.0220.0000.0000.0140.6740.5710.0351.000

Missing values

2023-10-22T15:51:40.038479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T15:51:40.222523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SexAgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2Thalassemia Type
005.998.2427.333.6352.7916.5920.9432.1411.10288.0074.032.013
1110.759.0235.521.5178.2516.7156.8933.474.99262.7870.953.382
2029.668.9233.423.8162.2416.1223.2334.265.47416.0565.152.583
3137.759.7729.793.3550.8126.6227.6530.046.04340.3359.083.432
4112.338.9930.914.1554.4218.3321.3036.578.25257.6865.612.523
5141.688.3228.944.1978.4116.5618.8735.4910.48451.3167.953.402
6041.057.5823.142.5572.6526.0029.3133.196.21129.3964.402.023
7122.998.8735.402.0261.4923.7738.7136.348.54438.8258.563.252
8120.989.7335.172.7253.4424.2032.1131.199.76173.2676.792.183
9024.268.7329.982.9852.1317.0729.2730.348.38363.3062.352.932
SexAgeHbPCVRBCMCVMCHMCHCRDWWBCPlthbAhbA2Thalassemia Type
2186012.178.8930.781.9375.7022.5235.8930.2111.94314.7170.492.123
2187013.798.3727.604.0355.4818.5819.0732.8010.25424.1672.743.622
218817.879.2326.672.1679.2018.8235.8632.217.24237.7464.172.522
2189012.857.2623.191.7579.4721.0136.2331.5211.25439.9169.223.113
2190118.919.9232.883.6834.3526.1425.2033.766.00107.0867.193.123
2191027.748.2029.224.3946.6219.7418.3932.7710.33340.1865.722.062
2192117.929.8835.242.1284.7416.5641.1934.508.01455.0264.192.392
2193126.278.7332.382.3869.9020.3433.4233.308.06237.4269.542.682
219407.517.7734.074.2769.5021.5618.1136.838.71136.8065.952.853
2195114.588.6035.003.1668.6220.0026.0330.4411.97196.1593.955.781